Recursive Recovery of Position and Orientation from Stereo Image Sequences Without Three-Dimensional Structures
Abstract
Traditional vision-based 3-D motion estimation algorithms for robots require given or calculated 3-D models while the motion is being tracked. We propose a high-speed extended-Kalman-filter-based approach that recovers position and orientation from stereo image sequences without prior knowledge as well as the procedure for the reconstruction of 3-D structures. Empowered by the use of the trifocal tensor, the computation step of 3-D models can be eliminated. The algorithm is thus more flexible and can be applied to a wide range of domains. The twist motion model is also adopted to parameterize the 3-D motion such that the motion representation in the proposed algorithm is robust and minimal. As the number of parameters to be estimated is reduced, our algorithm is more efficient, stable and accurate compared to traditional approaches. The proposed method has been verified using a real image sequence with ground truth.
Cite
Text
Yu et al. "Recursive Recovery of Position and Orientation from Stereo Image Sequences Without Three-Dimensional Structures." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.249Markdown
[Yu et al. "Recursive Recovery of Position and Orientation from Stereo Image Sequences Without Three-Dimensional Structures." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/yu2006cvpr-recursive/) doi:10.1109/CVPR.2006.249BibTeX
@inproceedings{yu2006cvpr-recursive,
title = {{Recursive Recovery of Position and Orientation from Stereo Image Sequences Without Three-Dimensional Structures}},
author = {Yu, Ying Kin and Wong, Kin-hong and Or, Siu-Hang and Chang, Michael Ming Yuen},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {1274},
doi = {10.1109/CVPR.2006.249},
url = {https://mlanthology.org/cvpr/2006/yu2006cvpr-recursive/}
}